





'''simple docstring'''




# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig






def 			_lowerCAmelCase     (    __snake_case    :					Dict					,				__snake_case    :					Any					,				__snake_case    :					Tuple=0     )	->	Any:
     # Format the message.
     if name is None:
          __A		:					Union[str, Any]						=			None
     else:
          __A		:					List[Any]						=			'.' * max(0					,				spaces - 2     ) + '# {:' + str(50 - spaces     ) + 's}'
          __A		:					Dict						=			fmt.format(__snake_case     )

     # Print and recurse (if needed).
     if isinstance(__snake_case					,				__snake_case     ):
          if msg is not None:
               print(__snake_case     )
          for k in val.keys():
               recursive_print(__snake_case					,				val[k]					,				spaces + 2     )
     elif isinstance(__snake_case					,				torch.Tensor     ):
          print(__snake_case					,				':'					,				val.size()     )
     else:
          print(__snake_case					,				':'					,				__snake_case     )






def 			_lowerCAmelCase     (    __snake_case    :					Union[str, Any]					,				__snake_case    :					List[str]					,				__snake_case    :					Optional[Any]					,				__snake_case    :					List[str]					,				__snake_case    :					List[str]     )	->	Union[str, Any]:
     # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
     # for compatibility with later versions of NVIDIA Megatron-LM.
     # The inverse operation is performed inside Megatron-LM to read checkpoints:
     # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
     # If param is the weight tensor of the self-attention block, the returned tensor
     # will have to be transposed one more time to be read by HuggingFace GPT2.
     __A		:					Optional[Any]						=			param.size()
     if checkpoint_version == 1.0:
          # version 1.0 stores [num_heads * hidden_size * num_splits, :]
          __A		:					Optional[Any]						=			(num_heads, hidden_size, num_splits) + input_shape[1:]
          __A		:					List[str]						=			param.view(*__snake_case     )
          __A		:					Tuple						=			param.transpose(0					,				2     )
          __A		:					Dict						=			param.transpose(1					,				2     ).contiguous()
     elif checkpoint_version >= 2.0:
          # other versions store [num_heads * num_splits * hidden_size, :]
          __A		:					Dict						=			(num_heads, num_splits, hidden_size) + input_shape[1:]
          __A		:					str						=			param.view(*__snake_case     )
          __A		:					Optional[Any]						=			param.transpose(0					,				1     ).contiguous()
     __A		:					Dict						=			param.view(*__snake_case     )
     return param






def 			_lowerCAmelCase     (    __snake_case    :					Optional[int]					,				__snake_case    :					str					,				__snake_case    :					Dict     )	->	Union[str, Any]:
     # The converted output model.
     __A		:					str						=			{}

     # old versions did not store training args
     __A		:					List[str]						=			input_state_dict.get('args'					,				__snake_case     )
     if ds_args is not None:
          # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
          # from pprint import pprint
          # pprint(vars(ds_args))

          __A		:					Tuple						=			ds_args.padded_vocab_size
          __A		:					str						=			ds_args.max_position_embeddings
          __A		:					str						=			ds_args.hidden_size
          __A		:					Any						=			ds_args.num_layers
          __A		:					List[Any]						=			ds_args.num_attention_heads
          __A		:					Union[str, Any]						=			ds_args.ffn_hidden_size
          # pprint(config)

     # The number of heads.
     __A		:					List[Any]						=			config.n_head
     # The hidden_size per head.
     __A		:					List[str]						=			config.n_embd // config.n_head
     # Megatron-LM checkpoint version
     if "checkpoint_version" in input_state_dict.keys():
          __A		:					Optional[Any]						=			input_state_dict['checkpoint_version']
     else:
          __A		:					List[str]						=			0.0

     # The model.
     __A		:					Any						=			input_state_dict['model']
     # The language model.
     __A		:					Tuple						=			model['language_model']
     # The embeddings.
     __A		:					Any						=			lm['embedding']

     # The word embeddings.
     __A		:					int						=			embeddings['word_embeddings']['weight']
     # Truncate the embedding table to vocab_size rows.
     __A		:					Union[str, Any]						=			word_embeddings[: config.vocab_size, :]
     __A		:					Any						=			word_embeddings

     # The position embeddings.
     __A		:					List[Any]						=			embeddings['position_embeddings']['weight']
     # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
     __A		:					Dict						=			pos_embeddings.size(0     )
     if n_positions != config.n_positions:
          raise ValueError(
              f'pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match'     )
     # Store the position embeddings.
     __A		:					Any						=			pos_embeddings

     # The transformer.
     __A		:					Any						=			lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

     # The regex to extract layer names.
     __A		:					Optional[Any]						=			re.compile(r'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'     )

     # The simple map of names for "automated" rules.
     __A		:					int						=			{
         'attention.dense': '.attn.c_proj.',
         'self_attention.dense': '.attn.c_proj.',
         'mlp.dense_h_to_4h': '.mlp.c_fc.',
         'mlp.dense_4h_to_h': '.mlp.c_proj.',
     }

     # Extract the layers.
     for key, val in transformer.items():
          # Match the name.
          __A		:					str						=			layer_re.match(__snake_case     )

          # Stop if that's not a layer
          if m is None:
               break

          # The index of the layer.
          __A		:					Any						=			int(m.group(1     )     )
          # The name of the operation.
          __A		:					List[Any]						=			m.group(2     )
          # Is it a weight or a bias?
          __A		:					str						=			m.group(3     )

          # The name of the layer.
          __A		:					Dict						=			f'transformer.h.{layer_idx}'

          # For layernorm(s), simply store the layer norm.
          if op_name.endswith('layernorm'     ):
               __A		:					str						=			'ln_1' if op_name.startswith('input'     ) else 'ln_2'
               __A		:					Union[str, Any]						=			val

          # Transpose the QKV matrix.
          elif (
              op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
          ) and weight_or_bias == "weight":
               # Insert a tensor of 1x1xDxD bias.
               __A		:					Dict						=			torch.tril(torch.ones((n_positions, n_positions)					,				dtype=torch.floataa     )     ).view(
                   1					,				1					,				__snake_case					,				__snake_case     )
               __A		:					Union[str, Any]						=			causal_mask

               # Insert a "dummy" tensor for masked_bias.
               __A		:					Any						=			torch.tensor(-1e4					,				dtype=torch.floataa     )
               __A		:					List[Any]						=			masked_bias

               __A		:					List[Any]						=			fix_query_key_value_ordering(__snake_case					,				__snake_case					,				3					,				__snake_case					,				__snake_case     )
               # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
               __A		:					List[Any]						=			out_val.transpose(0					,				1     ).contiguous()
               # Store.
               __A		:					List[Any]						=			out_val

          # Transpose the bias.
          elif (
              op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
          ) and weight_or_bias == "bias":
               __A		:					Any						=			fix_query_key_value_ordering(__snake_case					,				__snake_case					,				3					,				__snake_case					,				__snake_case     )
               # Store. No change of shape.
               __A		:					Optional[Any]						=			out_val

          # Transpose the weights.
          elif weight_or_bias == "weight":
               __A		:					Union[str, Any]						=			megatron_to_transformers[op_name]
               __A		:					Union[str, Any]						=			val.transpose(0					,				1     )

          # Copy the bias.
          elif weight_or_bias == "bias":
               __A		:					Optional[Any]						=			megatron_to_transformers[op_name]
               __A		:					Tuple						=			val

    # DEBUG.
     assert config.n_layer == layer_idx + 1

     # The final layernorm.
     __A		:					List[Any]						=			transformer['final_layernorm.weight']
     __A		:					int						=			transformer['final_layernorm.bias']

     # For LM head, transformers' wants the matrix to weight embeddings.
     __A		:					Union[str, Any]						=			word_embeddings

     # It should be done!
     return output_state_dict






def 			_lowerCAmelCase     (    )	->	Optional[Any]:
     # Create the argument parser.
     __A		:					List[Any]						=			argparse.ArgumentParser()
     parser.add_argument('--print-checkpoint-structure'					,				action='store_true'     )
     parser.add_argument(
         'path_to_checkpoint'					,				type=__snake_case					,				help='Path to the checkpoint file (.zip archive or direct .pt file)'					,				)
     parser.add_argument(
         '--config_file'					,				default=''					,				type=__snake_case					,				help='An optional config json file describing the pre-trained model.'					,				)
     __A		:					Optional[Any]						=			parser.parse_args()

     # Extract the basename.
     __A		:					List[Any]						=			os.path.dirname(args.path_to_checkpoint     )

     # Load the model.
     # the .zip is very optional, let's keep it for backward compatibility
     print(f'Extracting PyTorch state dictionary from {args.path_to_checkpoint}'     )
     if args.path_to_checkpoint.endswith('.zip'     ):
          with zipfile.ZipFile(args.path_to_checkpoint					,				'r'     ) as checkpoint:
               with checkpoint.open('release/mp_rank_00/model_optim_rng.pt'     ) as pytorch_dict:
                    __A		:					Optional[Any]						=			torch.load(__snake_case					,				map_location='cpu'     )
     else:
          __A		:					List[Any]						=			torch.load(args.path_to_checkpoint					,				map_location='cpu'     )

     __A		:					Dict						=			input_state_dict.get('args'					,				__snake_case     )

     # Read the config, or default to the model released by NVIDIA.
     if args.config_file == "":
          if ds_args is not None:
               if ds_args.bias_gelu_fusion:
                    __A		:					int						=			'gelu_fast'
               elif ds_args.openai_gelu:
                    __A		:					Dict						=			'gelu_new'
               else:
                    __A		:					int						=			'gelu'
          else:
               # in the very early days this used to be "gelu_new"
               __A		:					Optional[Any]						=			'gelu_new'

          # Spell out all parameters in case the defaults change.
          __A		:					str						=			GPTaConfig(
              vocab_size=5_02_57					,				n_positions=10_24					,				n_embd=10_24					,				n_layer=24					,				n_head=16					,				n_inner=40_96					,				activation_function=__snake_case					,				resid_pdrop=0.1					,				embd_pdrop=0.1					,				attn_pdrop=0.1					,				layer_norm_epsilon=1e-5					,				initializer_range=0.02					,				summary_type='cls_index'					,				summary_use_proj=__snake_case					,				summary_activation=__snake_case					,				summary_proj_to_labels=__snake_case					,				summary_first_dropout=0.1					,				scale_attn_weights=__snake_case					,				use_cache=__snake_case					,				bos_token_id=5_02_56					,				eos_token_id=5_02_56					,				)
     else:
          __A		:					int						=			GPTaConfig.from_json_file(args.config_file     )

     __A		:					Optional[Any]						=			['GPT2LMHeadModel']

     # Convert.
     print('Converting'     )
     __A		:					str						=			convert_megatron_checkpoint(__snake_case					,				__snake_case					,				__snake_case     )

     # Print the structure of converted state dict.
     if args.print_checkpoint_structure:
          recursive_print(__snake_case					,				__snake_case     )

     # Add tokenizer class info to config
     # see https://github.com/huggingface/transformers/issues/13906)
     if ds_args is not None:
          __A		:					List[Any]						=			ds_args.tokenizer_type
          if tokenizer_type == "GPT2BPETokenizer":
               __A		:					Tuple						=			'gpt2'
          elif tokenizer_type == "PretrainedFromHF":
               __A		:					Optional[int]						=			ds_args.tokenizer_name_or_path
          else:
               raise ValueError(f'Unrecognized tokenizer_type {tokenizer_type}'     )
     else:
          __A		:					int						=			'gpt2'

     __A		:					Optional[int]						=			AutoTokenizer.from_pretrained(__snake_case     )
     __A		:					List[str]						=			type(__snake_case     ).__name__
     __A		:					int						=			tokenizer_class

     # Store the config to file.
     print('Saving config'     )
     config.save_pretrained(__snake_case     )

     # Save tokenizer based on args
     print(f'Adding {tokenizer_class} tokenizer files'     )
     tokenizer.save_pretrained(__snake_case     )

     # Store the state_dict to file.
     __A		:					Dict						=			os.path.join(__snake_case					,				'pytorch_model.bin'     )
     print(f'Saving checkpoint to "{output_checkpoint_file}"'     )
     torch.save(__snake_case					,				__snake_case     )


####################################################################################################

if __name__ == "__main__":
  main()

####################################################################################################



'''simple docstring'''


import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							@require_torch
							def UpperCAmelCase_      (       self    ):
											# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
											# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
											# while running an external program

											# python one-liner segments

											# this must be loaded before socket.socket is monkey-patched
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= """
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
        """

											_SCREAMING_SNAKE_CASE					:			Dict							= """
mname = \"hf-internal-testing/tiny-random-bert\"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task=\"fill-mask\", model=mname)
print(\"success\")
        """

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= """
import socket
def offline_socket(*args, **kwargs): raise RuntimeError(\"Offline mode is enabled, we shouldn't access internet\")
socket.socket = offline_socket
        """

											# Force fetching the files so that we can use the cache
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= """hf-internal-testing/tiny-random-bert"""
											BertConfig.from_pretrained(__snake_case    )
											BertModel.from_pretrained(__snake_case    )
											BertTokenizer.from_pretrained(__snake_case    )
											pipeline(task="""fill-mask"""       ,   model=__snake_case    )

											# baseline - just load from_pretrained with normal network
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= [sys.executable, """-c""", """\n""".join([load, run, mock]    )]

											# should succeed
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= self.get_env()
											# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
											_SCREAMING_SNAKE_CASE					:			int							= """1"""
											_SCREAMING_SNAKE_CASE					:			str							= subprocess.run(__snake_case       ,   env=__snake_case       ,   check=__snake_case       ,   capture_output=__snake_case    )
											self.assertEqual(result.returncode       ,   0       ,   result.stderr    )
											self.assertIn("""success"""       ,   result.stdout.decode()    )






							@require_torch
							def UpperCAmelCase_      (       self    ):
											# python one-liner segments
											# this must be loaded before socket.socket is monkey-patched
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= """
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
        """

											_SCREAMING_SNAKE_CASE					:			Any							= """
mname = \"hf-internal-testing/tiny-random-bert\"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task=\"fill-mask\", model=mname)
print(\"success\")
        """

											_SCREAMING_SNAKE_CASE					:			List[Any]							= """
import socket
def offline_socket(*args, **kwargs): raise socket.error(\"Faking flaky internet\")
socket.socket = offline_socket
        """

											# Force fetching the files so that we can use the cache
											_SCREAMING_SNAKE_CASE					:			Tuple							= """hf-internal-testing/tiny-random-bert"""
											BertConfig.from_pretrained(__snake_case    )
											BertModel.from_pretrained(__snake_case    )
											BertTokenizer.from_pretrained(__snake_case    )
											pipeline(task="""fill-mask"""       ,   model=__snake_case    )

											# baseline - just load from_pretrained with normal network
											_SCREAMING_SNAKE_CASE					:			Tuple							= [sys.executable, """-c""", """\n""".join([load, run, mock]    )]

											# should succeed
											_SCREAMING_SNAKE_CASE					:			str							= self.get_env()
											_SCREAMING_SNAKE_CASE					:			Tuple							= subprocess.run(__snake_case       ,   env=__snake_case       ,   check=__snake_case       ,   capture_output=__snake_case    )
											self.assertEqual(result.returncode       ,   0       ,   result.stderr    )
											self.assertIn("""success"""       ,   result.stdout.decode()    )






							@require_torch
							def UpperCAmelCase_      (       self    ):
											# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
											# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
											# while running an external program

											# python one-liner segments

											# this must be loaded before socket.socket is monkey-patched
											_SCREAMING_SNAKE_CASE					:			Tuple							= """
from transformers import BertConfig, BertModel, BertTokenizer
        """

											_SCREAMING_SNAKE_CASE					:			str							= """
mname = \"hf-internal-testing/tiny-random-bert-sharded\"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
print(\"success\")
        """

											_SCREAMING_SNAKE_CASE					:			List[Any]							= """
import socket
def offline_socket(*args, **kwargs): raise ValueError(\"Offline mode is enabled\")
socket.socket = offline_socket
        """

											# baseline - just load from_pretrained with normal network
											_SCREAMING_SNAKE_CASE					:			List[str]							= [sys.executable, """-c""", """\n""".join([load, run]    )]

											# should succeed
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.get_env()
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= subprocess.run(__snake_case       ,   env=__snake_case       ,   check=__snake_case       ,   capture_output=__snake_case    )
											self.assertEqual(result.returncode       ,   0       ,   result.stderr    )
											self.assertIn("""success"""       ,   result.stdout.decode()    )

											# next emulate no network
											_SCREAMING_SNAKE_CASE					:			List[str]							= [sys.executable, """-c""", """\n""".join([load, mock, run]    )]

											# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
											# env["TRANSFORMERS_OFFLINE"] = "0"
											# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
											# self.assertEqual(result.returncode, 1, result.stderr)

											# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
											_SCREAMING_SNAKE_CASE					:			Dict							= """1"""
											_SCREAMING_SNAKE_CASE					:			int							= subprocess.run(__snake_case       ,   env=__snake_case       ,   check=__snake_case       ,   capture_output=__snake_case    )
											self.assertEqual(result.returncode       ,   0       ,   result.stderr    )
											self.assertIn("""success"""       ,   result.stdout.decode()    )






							@require_torch
							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= """
from transformers import pipeline
        """
											_SCREAMING_SNAKE_CASE					:			int							= """
mname = \"hf-internal-testing/tiny-random-bert\"
pipe = pipeline(model=mname)
        """

											_SCREAMING_SNAKE_CASE					:			Any							= """
import socket
def offline_socket(*args, **kwargs): raise socket.error(\"Offline mode is enabled\")
socket.socket = offline_socket
        """
											_SCREAMING_SNAKE_CASE					:			Dict							= self.get_env()
											_SCREAMING_SNAKE_CASE					:			Any							= """1"""
											_SCREAMING_SNAKE_CASE					:			str							= [sys.executable, """-c""", """\n""".join([load, mock, run]    )]
											_SCREAMING_SNAKE_CASE					:			List[Any]							= subprocess.run(__snake_case       ,   env=__snake_case       ,   check=__snake_case       ,   capture_output=__snake_case    )
											self.assertEqual(result.returncode       ,   1       ,   result.stderr    )
											self.assertIn(
											    """You cannot infer task automatically within `pipeline` when using offline mode"""       ,   result.stderr.decode().replace("""\n"""       ,   """"""    )       ,   )






							@require_torch
							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= """
from transformers import AutoModel
        """
											_SCREAMING_SNAKE_CASE					:			Tuple							= """
mname = \"hf-internal-testing/test_dynamic_model\"
AutoModel.from_pretrained(mname, trust_remote_code=True)
print(\"success\")
        """

											# baseline - just load from_pretrained with normal network
											_SCREAMING_SNAKE_CASE					:			List[str]							= [sys.executable, """-c""", """\n""".join([load, run]    )]

											# should succeed
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= self.get_env()
											_SCREAMING_SNAKE_CASE					:			List[str]							= subprocess.run(__snake_case       ,   env=__snake_case       ,   check=__snake_case       ,   capture_output=__snake_case    )
											self.assertEqual(result.returncode       ,   0       ,   result.stderr    )
											self.assertIn("""success"""       ,   result.stdout.decode()    )

											# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= """1"""
											_SCREAMING_SNAKE_CASE					:			Any							= subprocess.run(__snake_case       ,   env=__snake_case       ,   check=__snake_case       ,   capture_output=__snake_case    )
											self.assertEqual(result.returncode       ,   0       ,   result.stderr    )
											self.assertIn("""success"""       ,   result.stdout.decode()    )




